"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ 2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on four texture and five object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance via extensive tests on the PASCAL database, for which ground-truth object localization information is available. Our experiments demonstrate that image representations based on distributions of local features are surprisingly effective for classification of texture and object images under challenging real-world conditions, including significant intra-class variations and substantial background clutter.

"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech-101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images. 1.

"... We present a simple texture synthesis algorithm that is well-suited for a specific class of naturally occurring textures. This class includes quasi-repeating patterns consisting of small objects of familiar but irregular size, such as flower fields, pebbles, forest undergrowth, bushes and tree branc ..."

We present a simple texture synthesis algorithm that is well-suited for a specific class of naturally occurring textures. This class includes quasi-repeating patterns consisting of small objects of familiar but irregular size, such as flower fields, pebbles, forest undergrowth, bushes and tree branches. The algorithm starts from a sample image and generates a new image of arbitrary size the appearance of which is similar to that of the original image. This new image does not change the basic spatial frequencies the original image; instead it creates an image that is a visually similar, and is of a size set by the user. This method is fast and its implementation is straightforward. We extend the algorithm to allow direct user input for interactive control over the texture synthesis process. This allows the user to indicate large-scale properties of the texture appearance using a standard painting-style interface, and to choose among various candidate textures the algorithm can create by performing different number of iterations.

...out effects due to illumination and/or surface geometric structure. If this problem is solved reliably, one could design an algorithm capable of synthesizing view- and illumination dependent textures =-=[4]-=-. Another important area of future work is direct synthesis of texture over 3D geometry. Finally, we believe that it would be beneficial to invest work into theoretical analysis of simple texture synt...

"... . This paper presents how the image-based rendering technique of viewdependent texture-mapping (VDTM) can be efficiently implemented using projective texture mapping, a feature commonly available in polygon graphics hardware. VDTM is a technique for generating novel views of a scene with approximate ..."

. This paper presents how the image-based rendering technique of viewdependent texture-mapping (VDTM) can be efficiently implemented using projective texture mapping, a feature commonly available in polygon graphics hardware. VDTM is a technique for generating novel views of a scene with approximately known geometry making maximal use of a sparse set of original views. The original presentation of VDTM in by Debevec, Taylor, and Malik required significant per-pixel computation and did not scale well with the number of original images. In our technique, we precompute for each polygon the set of original images in which it is visibile and create a &quot;view map&quot; data structure that encodes the best texture map to use for a regularly sampled set of possible viewing directions. To generate a novel view, the view map for each polygon is queried to determine a set of no more than three original images to blended together in order to render the polygon with projective texture-mapping. Invisible t...

...of each polygon and distill a unified view-dependent function of its appearance, rather than the raw set of original views. One such representation is the Bidirectional texture function, presented in =-=[1]-=-, or a yet-to-be-presented form of compressed light field. Both techniques will require new rendering methods in order to render the distilled representations in real time. Extensions of techniques su...

"... Realism in computer-generated images requires accurate input models for lighting, textures and BRDFs. One of the best ways of obtaining high-quality data is through measurements of scene attributes from real photographs by inverse rendering. However, inverse rendering methods have been largely limit ..."

Realism in computer-generated images requires accurate input models for lighting, textures and BRDFs. One of the best ways of obtaining high-quality data is through measurements of scene attributes from real photographs by inverse rendering. However, inverse rendering methods have been largely limited to settings with highly controlled lighting. One of the reasons for this is the lack of a coherent mathematical framework for inverse rendering under general illumination conditions. Our main contribution is the introduction of a signal-processing framework which describes the reflected light field as a convolution of the lighting and BRDF, and expresses it mathematically as a product of spherical harmonic coefficients of the BRDF and the lighting. Inverse rendering can then be viewed as deconvolution. We apply this theory to a variety of problems in inverse rendering, explaining a number of previous empirical results. We will show why certain problems are ill-posed or numerically ill-conditioned, and why other problems are more amenable to solution. The theory developed here also leads to new practical representations and algorithms. For instance, we present a method to factor the lighting and BRDF from a small number of views, i.e. to estimate both simultaneously when neither is known.

"... In this paper we present a method for recovering the reflectance properties of all surfaces in a real scene from a sparse set of photographs, taking into account both direct and indirect illumination. The result is a lighting-independent model of the scene's geometry and reflectance properties, ..."

In this paper we present a method for recovering the reflectance properties of all surfaces in a real scene from a sparse set of photographs, taking into account both direct and indirect illumination. The result is a lighting-independent model of the scene&apos;s geometry and reflectance properties, which can be rendered with arbitrary modifications to structure and lighting via traditional rendering methods. Our technique models reflectance with a lowparameter reflectance model, and allows diffuse albedo to vary arbitrarily over surfaces while assuming that non-diffuse characteristics remain constant across particular regions. The method&apos;s input is a geometric model of the scene and a set of calibrated high dynamic range photographs taken with known direct illumination. The algorithm hierarchically partitions the scene into a polygonal mesh, and uses image-based rendering to construct estimates of both the radiance and irradiance of each patch from the photographic data. The algorithm computes the expected location of specular highlights, and then analyzes the highlight areas in the images by running a novel iterative optimization procedure to recover the diffuse and specular reflectance parameters for each region. Lastly, these parameters are used in constructing high-resolution diffuse albedo maps for each surface.

...s (diffuse color, shininess, etc.) of each surface. Unfortunately, such reflectance property information is not directly available from the scene geometry or from photographs. Considerable work (e.g. =-=[32, 16, 5, 27, 21]-=-) has been done to estimate reflectance properties of real surfaces in laboratory settings from a dense set of measurements. However, reflectance properties of real scenes are usually spatially varyin...

"... In free-viewpoint video, the viewer can interactively choose his viewpoint in 3-D space to observe the action of a dynamic realworld scene from arbitrary perspectives. The human body and its motion plays a central role in most visual media and its structure can be exploited for robust motion estimat ..."

In free-viewpoint video, the viewer can interactively choose his viewpoint in 3-D space to observe the action of a dynamic realworld scene from arbitrary perspectives. The human body and its motion plays a central role in most visual media and its structure can be exploited for robust motion estimation and efficient visualization. This paper describes a system that uses multi-view synchronized video footage of an actor&apos;s performance to estimate motion parameters and to interactively re-render the actor&apos;s appearance from any viewpoint.

"... We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space o ..."

We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space of acquired BRDFs to create new BRDFs. We treat each acquired BRDF as a single high-dimensional vector taken from a space of all possible BRDFs. We apply both linear (subspace) and non-linear (manifold) dimensionality reduction tools in an effort to discover a lowerdimensional representation that characterizes our measurements. We let users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space. On the low-dimensional manifold, movement along these directions produces novel but valid BRDFs.